DEV Community: Rahul Goel

Five Conditions, Twelve Wishes: Designing an Option-Selling Rule Set Under a Builder Cap

Rahul Goel — Thu, 12 Nov 2026 06:13:45 +0000

Five Conditions, Twelve Wishes: Designing an Option-Selling Rule Set Under a Builder Cap
Sit down to build a multi-leg option-selling strategy in a no-code builder and you will hit the same wall within the first hour. You have twelve ideas for rules. The builder gives you room for five. Some tools cap the number of entry and exit conditions per strategy, and the cap is usually small enough that you feel it before you finish the first draft.

Most people react in one of two ways. They squeeze in every rule by making each one vague, or they pick the five that feel most clever and hope for the best. Both approaches produce strategies that are hard to trust. A vague rule fires at the wrong time. A clever rule fires so rarely that it never earns its slot.

This article gives you a method for the choice. It covers how to count what you really have, how to rank rules by what they protect, which ones to drop first, which ones you can move out of the builder entirely, and how to tell when the cap is a sign that you have outgrown the tool. The examples are built around weekly-expiry index selling on NIFTY and SENSEX, but the logic holds for any multi-leg short-premium setup.

What a condition cap really limits
A cap on conditions is a cap on decisions your strategy can make by itself. It is not a cap on how good the strategy can be. That difference matters, because a short-premium strategy needs surprisingly few decisions to work, and a strategy with many decisions is often just a strategy with many ways to fail.

First, learn how your builder counts. This is the step people skip. Some builders count one condition per rule, no matter how many legs it applies to. Others count a stop-loss on each leg as a separate condition. Others count the entry time as a condition and the exit time as another. If you have four legs and each has its own stop-loss and its own target, a builder that counts per leg has used eight slots before you have written anything about entry. Read the vendor's current documentation and test with a dummy strategy. Do not rely on a forum post, because these rules change between versions.

Second, notice what a cap does to your thinking. It forces you to say which rules you would defend in front of a sceptical friend. That is useful. A strategy where you can name the job of every condition is easier to debug than a strategy with fifteen rules where nobody remembers why the ninth one exists. Treat the cap as a design constraint, the way a page limit helps a writer, and the first draft of your rule set will get shorter and better.

Take inventory before you rank anything
Write down every rule you want, in plain words, before you open the builder. Do not filter. Include the ones you think are silly. A typical wish list for a four-leg NIFTY weekly-expiry strategy (a short strangle with two bought hedges, which is an iron condor) looks like this.

Enter at a fixed time, say 09:20.
Skip the day if India VIX is above a set level.
Pick the short strikes by premium (nearest to Rs 40, for instance), not by distance.
Buy hedge legs a fixed number of points further out.
Stop-loss on each short leg at a set percentage of its entry price.
Stop-loss on the whole position at a set rupee loss.
Trail the combined stop once profit passes a threshold.
Exit everything at a fixed time, say 15:15.
Re-enter once if the first position is stopped out.
Skip the day if the previous session moved more than a set percentage.
Exit everything if spot moves a set percentage from the entry level.
Allow one trade per day only That is twelve. Now sort them into four kinds, because the kind matters more than the rule.

Look at what this table already tells you. Five of the twelve are risk rules, and they are the ones fighting for the same few slots. Structure and schedule rules often do not need slots at all, which is the subject of a later section. Filters are the easiest to cut, as you will see.

The exit-first rule: rank by what a condition protects
Here is the ranking principle that does most of the work. Rank every condition by the size of the loss it prevents on your worst day, not by how often it triggers or how elegant it is. Short-premium strategies make small gains most days and give back large amounts on a few. The rules that cap the large losses are the ones that keep the account alive long enough for the small gains to add up.

That gives a priority order that surprises some people, because entry rules come last.

Priority 1: a hard stop on the whole position
Pick one rule that ends the trade no matter what else is going on. A combined stop-loss in rupees is the usual choice because it is one condition no matter how many legs you have. If you keep only one risk rule, keep this one. Without it, a strategy depends on every other rule working perfectly on the worst day, and that is when they tend not to.

Priority 2: a time exit
An exit at a fixed clock time does two jobs. It closes the position before the last chaotic stretch of expiry day, and it removes the need to decide anything at 15:10 when you are tired. It also ends the trade if nothing else did. Many builders treat this as a setting rather than a condition. If yours does, you get it free. If not, it still earns a slot.

Priority 3: profit protection
This is where the desk data is useful. EliteAlgo's study of 163 NIFTY and SENSEX weekly expiry sessions between January 2025 and July 2026 looked at a short ATM straddle entered at 09:20. On the 159 usable days, the median give-back from the intraday peak profit to the close was 42.8% of that peak. On 86 of the 159 days (54%) the position gave back more than 30% of its peak. On 48 days (30%) a position that had been in profit at some point finished the day as a loss.

Those figures describe a plain straddle with no protection, so they are not a forecast for your strategy. They do show how common it is for a winning position to melt. Some form of profit lock, whether a trailing stop on the combined P&L or a simple target, is the second-best use of a slot after the hard stop. If you can afford only one of the two, the target is cheaper to build and the trail is usually more effective, but test both before trusting either.

Priority 4: a per-leg rule, only if the legs behave differently
Stops on each short leg feel safe. They are also the most expensive rule in the list when the builder counts per leg, and they interact badly with a combined stop. Once one leg is stopped, the remaining leg can lose money quickly while the combined rule no longer describes your position. If the structure is hedged and the combined stop exists, the per-leg stop adds little and costs several slots. Drop it first when you are short of space, then check what changes.

Priority 5: the single best filter
You get at most one filter, and it has to pass the test in the next section.

What to drop first, and why
When you must cut, cut in this order: re-entry, then secondary filters, then per-leg stops, then fancy strike-selection logic. Here is the reasoning for each.

Re-entry goes first. A rule that opens a second position after a loss doubles your exposure on exactly the days that went wrong. It also needs several conditions of its own: a trigger, a time window and a size. If you want to test re-entry later, do it as a separate strategy, not as a clause inside the first one.

Secondary filters go second. Rule 10, skipping days after a big previous-session move, and rule 2, the VIX cut-off, are both filters. Keep the one with better evidence, drop the other. Judge a filter by how many days it changes. The desk data has a useful reference here. Across the 163 sessions, only 11 had a VIX reading above 20 at the 09:20 snapshot. That is about 1 day in 15 (6.7%). A rule that says "skip if VIX is above 20" would have changed your behaviour on 11 sessions out of 163. If you use that slot on it, you spend a precious condition on a rule that is idle for more than 93% of the sessions.

Compare that to a cut-off at 14. In the same study, 35 sessions fell in the 14-17 band, 24 in 17-20 and 11 above 20, so a cut-off at 14 would have skipped 70 of 163 sessions, which is 43%. That changes the strategy a lot. It might also be the right call, but now you are really designing a different strategy, one that trades only in calm conditions, and you should test it on its own merits. The point is that how often a filter fires should drive whether it deserves a slot. A rule that triggers on 7% of days has little effect on the results, good or bad. A rule that triggers on 43% of days is a second strategy hiding inside the first.

Afternoon-only rules are weak. The same study tracked the ATM straddle premium as a share of its 09:16 level. It fell from 100% to about 88.6% by 11:16, then sat in an 88% to 93% band until the close. The strike is fixed at the 09:20 level while spot keeps moving, so decay and re-pricing roughly cancel. A rule that waits for "more afternoon decay" has little support in this data.

Fancy strike selection goes last. Choosing strikes by a target premium instead of a fixed distance sounds sensible, because premiums move with volatility. But in many builders the choice is a setting made at strategy creation and not a counted condition. Check before you cut it. If it does count, a plain rule such as "sell the strike at a fixed distance from ATM" may be good enough, because the VIX band already changes the premium you collect. The desk data shows the ATM straddle premium as a share of spot rising from a mean of 0.38% when VIX is under 12 to 0.83% when VIX is over 20. Strikes picked by distance will collect less in calm markets and more in stressed ones. Whether that is a feature or a bug depends on your risk limits, not on the builder.

Move rules out of the builder altogether
Many conditions do not need a slot because you can move them elsewhere. Four places are worth a look.

Structure. A hedge leg is a position, not a rule. Rule 4 in the list above, buying hedges a fixed distance out, becomes part of the strategy's shape and uses no condition slots in most builders. The same goes for the number of lots. Some risk rules can also be built into the structure: a bought wing defines a maximum loss per lot without any stop condition at all. That is a strong reason to hedge a strategy when you are short on conditions.

Schedule. Entry time and exit time are often fields on the strategy, not conditions. A start time of 09:20 and an end time of 15:15 might cost nothing. Check, because it changes the maths.

The calendar. "One trade per day" and "only on expiry day" can be handled by choosing the day the strategy is active. You do not need a condition to stop a strategy from running on Wednesday if you only switch it on for Thursday.

You. A filter you apply by hand costs zero slots. If your rule is "do not run the strategy on the morning of a major scheduled event," then the cheapest and most reliable implementation is that you do not switch the strategy on that morning. This is not cheating. It is a deliberate human override, with a written rule, applied before the market opens and not after the loss. The risk is that you apply it unevenly, so write the rule down and keep a log of the days you used it.

Moving rules out of the builder is also the point where people start looking at what else is on the market. If the cap is the main thing slowing you down, it makes sense to compare builders on that one dimension. One published list of Zerodha Streak alternatives is a reasonable place to see what other tools offer, though condition limits change between versions, so check each vendor's current documentation and test with a dummy strategy before you decide anything.

A worked example: from twelve wishes to five slots
Take the twelve-rule wish list and assume a builder that counts one slot per risk or filter rule, counts a per-leg stop once per leg, and treats the entry time, exit time and strike selection as settings that cost nothing. This is an illustrative counting scheme, not a description of any product.

Twelve wishes came down to four slots with one spare. The strategy has not lost its risk control. It has a hard stop, a trail, a spot-move exit, a hedge structure, a time exit and one filter. What it has lost is re-entry, the second filter and the per-leg stops, and each of those has a stated reason.

Now decide what to do with the spare slot. A good use is a second protective rule, such as a profit target that closes the position when it reaches a set rupee gain. Another good use is to leave it empty and wait until a backtest shows a clear weakness. An empty slot is not wasted. It is room for the first thing your testing teaches you.

The drop-one test: prove each condition earns its place
Once you have a five-slot design, test it by removing one condition at a time and running the same backtest over the same period. You will end up with a table like this, filled in with your own numbers.

Read the table with three questions. Did the worst day get worse when the rule was removed? A risk rule that does not improve the worst day is not doing its job. Did the rule fire often enough to matter? If it fired on four days in a year, its effect on net P&L is mostly luck, and the number of days is the thing to look at, not the rupee figure. Did removing it change net P&L a lot but the worst day not at all? Then it is an optimisation rule, not a protection rule, and it ranks below the others.

Be careful with the result. A backtest is a record of what a rule would have done in the past data, with the costs and fill assumptions you gave it. Slippage, brokerage, taxes and the gap between a backtest fill and a live fill all matter. A rule that looks useful by a thin margin on a backtest often vanishes after costs. When two versions are close, choose the simpler one.

Signs you have outgrown a no-code builder
A cap on conditions is a normal limit of a product designed for fast setup. Hitting it once is not a reason to change tools. These are the signs that the cap has become a real problem.

Your best design needs more than seven or eight conditions, and each one passes the drop-one test. If every rule is earning its keep and you still have to cut, the tool is limiting you and not your thinking.
You keep splitting one idea into several strategies to get around the cap. Running two linked strategies that must act together, with nothing coordinating their stops, creates risk that neither strategy shows on its own screen.
Your key rule depends on something the builder cannot read. Examples include a rule that depends on your total exposure across several strategies, or on option-chain data the builder does not expose.
You are applying manual overrides every day. A few overrides are fine. If you are intervening most days, the strategy as built does not match the strategy as traded, and your backtest is describing something you do not run.
Your risk limit is at the account level, not the strategy level. A cap on total daily loss across strategies is a different kind of rule from a strategy stop, and many builders do not offer it.

If two or more of those are true, the useful question is no longer how to fit within five conditions. It is what you will move to, and how you will check that the new setup behaves the same as the old one. Write down the rules before you move, run the same days through both, and compare the fills and the exits. Switching tools in the middle of a drawdown is a bad time to find out that the new tool counts a stop differently.

There are three common routes. You can move to a more flexible builder, to a platform that accepts custom logic, or to your own code connected to a broker's API. Each trades convenience for control. If you take the code route, expect to spend time on order handling, reconciliation and logging, which the builder was doing for you without being asked. Do not underestimate that work. Whichever route you take, read SEBI's retail algo circulars on sebi.gov.in directly, because platform rules on orders and approvals have been changing.

Frequently Asked Questions
How many conditions does a good option-selling strategy need?

Fewer than most people think. A hedged short-premium strategy can work with a hard stop on the combined position, a time exit, one profit-protection rule and one filter. Extra conditions should each pass a drop-one test. If removing a rule does not change the worst day or the net result, it does not belong.

Should I use a stop-loss on each leg or on the whole position?

For a hedged multi-leg position, the combined stop is usually the better first choice because it describes the loss you actually care about and takes one slot. Per-leg stops can help when legs behave very differently, but they often act before the combined rule would and cost several slots. Test both on the same days and compare the worst day, not only the total.

Is re-entry after a stop-loss a good idea?

It is a separate strategy, not a feature. It adds exposure after a loss, and it needs its own trigger and size. If you want to test it, build it as its own version and compare it with the version that has no re-entry over the same dates.

Can I skip trading on high-VIX days?

You can, but check how many days the filter would change. In a study of 163 NIFTY and SENSEX expiry sessions, only 11 had a 09:20 VIX above 20, so that cut-off rarely fires. A cut-off in the low teens would skip a large share of days and is effectively a different strategy. Decide which you mean.

What if my broker or platform does not allow more than five conditions?

First check how it counts, since per-leg rules can use several slots. Then move structure, timing and calendar rules out of the condition list. If the strategy still needs more after that and every extra rule passes testing, the cap may mean it is time to look at a more flexible tool.

How do I know if a condition is worth keeping after costs?

Run the backtest with realistic brokerage, taxes and slippage, then run it again without the condition. If the gain from the rule is smaller than the uncertainty in your cost assumptions, treat it as unproven and leave it out.

Conclusion
A condition cap is a prompt to decide what matters. Rank rules by the loss they prevent on the worst day. Keep a hard stop, a time exit and a profit-protection rule before anything else. Count how often a filter actually fires, and be suspicious of rules that sit idle on most days or that change the character of the whole strategy. Move structure, schedule and calendar rules out of the builder, and use your own written override for the rest. Then prove each remaining condition by removing it and looking at the worst day.

If that process still leaves you with a design that needs more than the cap allows, you have found a real limit, and it is fine to move on. Just move deliberately, with your rules written down and a plan to compare old and new behaviour on the same days.

Algo trading carries a real risk of loss, including the loss of more than you expected on a bad day. EliteAlgo is analytical software and not an investment adviser. Nothing in this article is investment advice, a recommendation to trade, or a promise of any result. Test any rule set on paper and with small size before you rely on it.

How to Build Your Own ATM Straddle Premium Series From Option-Chain Data

Rahul Goel — Wed, 11 Nov 2026 06:12:00 +0000

Most traders look at a straddle premium that someone else built. A platform draws the line, and the trader accepts it. That works until the line does something odd at 11:40 and you cannot tell whether the market did it or the code did.

Building the series yourself fixes that. It takes an afternoon, needs no special software, and teaches you more about how weekly options price than any amount of reading. This article walks through the whole job: what data you need, how to pick the strike, why "fixed ATM" and "rolling ATM" give different lines, how to patch missing minutes, and how to stop one bad tick from wrecking a day.

The examples use NIFTY and SENSEX weekly expiry, because that is where the data is dense and the question is most useful. The steps are described in plain language. You can follow them in a spreadsheet or in a short Python script, and I note where each tool is easier.

What You Are Actually Building
An ATM straddle premium series is one number per minute: the price of the call plus the price of the put at the at-the-money strike, for a given expiry. If the call is 62 and the put is 58, the straddle is 120. Plotted across the day, those numbers form a line. That line tells you how much the market is charging for a move of the index, and how that charge changes as the session unfolds.

The definition sounds simple, but three decisions hide inside it. Which strike counts as "at the money" at a given minute? Do you keep that strike for the day or let it change? And what do you do when the data has holes? Each choice changes the shape of the line, sometimes a lot. Two people with the same raw data can build two different charts and both think they are right.

Why bother when charts already exist
Three reasons. First, you control the definition, so you know what the line means. Second, you can test your own ideas, such as a different entry time or a different strike rule, without waiting for a vendor to add a feature. Third, you learn the failure modes of option data, which are the same ones that will bite your live orders later.

The Data You Need
Before the raw inputs, one question: what should the finished product look like, and how is it read? A guide to reading an ATM straddle chart shows what a clean series looks like and what the rises, falls and flat stretches mean, which gives you a target to build towards. This article covers the build side.

You need three columns of raw input, at one-minute resolution, for each trading day.

The first is the underlying spot (or the index level) at each minute. The second is the call price at each strike you might use. The third is the put price at the same strikes. Ideally you want last traded price for each minute, plus bid and ask if you can get them, because the mid of bid and ask is steadier than the last trade.

You also need the expiry date for each option, so you can pick the nearest weekly contract, and the strike step. NIFTY strikes are generally spaced 50 points apart near the money, and SENSEX strikes 100 points apart. Check the current contract specifications on the exchange website, because these can change. The NSE website publishes the contract details for NIFTY.

Input Why it matters What goes wrong
Spot at each minute Decides which strike is ATM Stale spot makes the ATM strike wrong
Call and put price per strike Gives the straddle value Gaps and one-sided quotes
Bid and ask, if available Lets you use mid price Wide spreads late in the day
Expiry date Picks the right contract Mixing weekly and monthly expiry
Strike step Rounds spot to a strike Wrong step gives a strike that does not exist
Store each day in its own file or sheet tab. Do not mix days, and do not mix expiries. The most common reason for a strange chart is two contracts quietly sharing one column.

Choosing the Strike: The ATM Rule and Fixed vs Rolling
Step one: find the ATM strike
The ATM strike is the strike closest to spot. The pseudo-steps are short.

Take the spot at the minute you want.
Divide by the strike step (50 for NIFTY).
Round to the nearest whole number.
Multiply back by the strike step.
If NIFTY spot is 24,533, divide by 50 to get 490.66, round to 491, and multiply by 50 to get 24,550. The ATM strike is 24,550. In a spreadsheet this is one formula using the rounding function. In Python it is one line.

The trap is the halfway case. If spot is exactly between two strikes, rounding rules differ between tools, and your strike may flip on a tiny move. That matters in the rolling version below. Pick one rule, such as round half up, and use it every time so your series stays consistent.

Which spot should you use?

Use the spot at the same minute as your option prices. If your option data is the close of each one-minute bar, use the spot close for that bar. A common mistake is pairing the option close at 09:20 with the spot at 09:19, which pushes the ATM strike off by a notch on fast days. Align timestamps first, and check them against a few minutes you remember.

For expiry-day work, many traders use the futures price or a synthetic forward instead of spot, because the forward is what the options actually track. The difference is small for weekly contracts near expiry, but it is worth knowing. If you do use the forward, say so in your notes, because your ATM strike may differ from a spot-based chart by one step.

Step two: fixed ATM or rolling ATM
This is the decision that changes the chart the most, and it deserves its own section.

Fixed ATM. You pick the ATM strike once, at your entry time, and track that call and put for the rest of the day. The strike does not change even if spot moves 200 points away from it. This matches what a seller actually holds: a short straddle at one strike. It answers the question, "What happened to the position I entered?"

Rolling ATM. At each minute you recompute the ATM strike and read the straddle at whatever strike is ATM then. The line always shows the price of a fresh at-the-money straddle. It answers a different question: "What is the market charging for a straddle right now?"

The two lines agree at the start and drift apart as spot moves. A fixed-strike straddle gains value on the winning side as spot moves away, and loses premium on the other. A rolling straddle jumps when the strike changes, because you are now reading a different pair of options.

A desk study by EliteAlgo, covering 163 NIFTY and SENSEX weekly expiry sessions between January 2025 and July 2026, built a fixed-strike series. The strike was set at the 09:20 ATM level, and the premium was tracked as a share of the 09:16 entry premium, averaged across all sessions. The mean line did not fall in a smooth slope to zero. It dropped from 100% to about 88.6% by 11:16, then moved sideways between roughly 88% and 93% for the whole afternoon.

That shape surprises people who expect a straight theta slide. The reason is the fixed strike. Time decay lowers the premium, but spot keeps walking away from the strike, and the options re-price upward as that happens. The two forces net out. A rolling series would show a different, more continuous decline, because it keeps resetting to the new ATM. Neither is wrong, but you must know which one you built before you draw a conclusion from it.

Adding the Legs, Filling Gaps and Cleaning Ticks
Step three: add the call and the put
With the strike fixed for the minute, the straddle is call price plus put price. This sounds trivial. Four small rules keep it honest.

Use mid price when you have bid and ask, and use last traded price only when you do not. Last trade can be minutes old on a quiet strike, and a stale last price on one leg and a fresh one on the other gives a straddle that never existed.

Make sure both legs are for the same expiry and the same strike. A spreadsheet lookup that matches only on strike will happily pull a monthly contract into a weekly series.

Skip the minute if one leg is missing, rather than filling it with zero. A zero for the put turns a 120 straddle into a 62 straddle for one minute, and that single bad number can distort a day's statistics.

Record the strike next to each value. When you later find a spike, the strike column tells you immediately whether it was a real move or a strike change.

Step four: handle missing minutes
Real option data has holes. A strike that is not trading in a given minute produces no bar. Late in the day, far from the money or on a quiet session, gaps are common. You need a policy, and the policy must be written down before you look at the results, so that you do not pick the one that flatters your idea.

There are three honest options.

Forward-fill with a limit. Carry the last known price forward for a short time, say up to two or three minutes. Beyond that, leave the cell empty. This respects the fact that a price which has not changed is still the price, but stops a stale value from living for an hour.

Interpolate between neighbours. If a single minute is missing between two good ones, use the average of the two. This is reasonable for single gaps and risky for longer ones, because it smooths away movement that really happened.

Leave it empty and let the chart break. A gap in the line is information. It tells you the data was thin. For most analysis you can still compute daily statistics with the empty cells ignored.

The policy I suggest is simple: forward-fill up to three minutes, never interpolate, and flag every filled cell with a second column. Then, when you compute any statistic, report how many cells were filled. If more than a small share of a day's cells were filled, treat that day with caution or drop it.

The opening minutes are noisy because the book is thin and spreads are wide. A 09:15 price can be far from a 09:16 price for reasons that have nothing to do with value. Many traders therefore anchor their series at 09:16 or later, and some avoid 09:15 entries altogether. The last minutes before the close have a similar problem: prices of deep-in-the-money legs can freeze and the near-ATM options can move in large steps. Decide in advance which window you trust, and cut the series to that window.

Step five: clean bad ticks
A bad tick is a price that is not real. It might be a mis-keyed trade, a late-reported trade, or a vendor glitch. In an option series, one bad tick on a ₹60 option can easily look like a 30% move. If you build statistics on top of it, such as the maximum premium of the day or the peak profit of a short straddle, a single bad tick changes the answer.

Here are the checks that catch most of them.

The put-call parity check. For a given strike, call minus put should be roughly equal to the forward minus the strike, give or take a few points. If, at the same minute, the implied relationship is far off, one leg is probably wrong. You do not need to be exact. You are looking for obvious breaks, such as a 30-point mismatch on a strike where the usual mismatch is 2.

The one-minute jump check. Compare each minute with the previous one. If the straddle changes by more than a chosen threshold, flag it. A sensible threshold is a multiple of the typical one-minute change for that day. For example, flag any move larger than six times the median absolute one-minute change. Real fast markets will trip this too, so do not delete flagged points automatically. Look at them.

The reversal check. A genuine move usually does not reverse completely in the next minute. If the value jumps up and returns to its old level one minute later, and spot barely moved, it is likely a bad print. A real spike in premium generally comes with a visible move in spot or a visible change in the other leg.

The bound check. A call cannot be worth more than spot, and an option cannot be worth less than its intrinsic value on expiry day, apart from tiny rounding. Violations signal a data fault. On expiry day, as time runs out, an ATM straddle should converge towards the distance between spot and the strike, plus a small amount of time value.

What to do with a flagged point.

Do not silently delete. Write the flagged timestamps to a separate list with the reason. Replace the value with an empty cell, or with the neighbour average if it is a single minute and the reversal check says it was a bad print. Keep the raw file untouched. Six months later, when you wonder why a day looks odd, the log tells you what you did.

Putting It Together: Spreadsheet and Python
A spreadsheet version
You can do all of this in a spreadsheet for a handful of days. Here is a plain outline.

One tab per day. Columns: time, spot, strike, call, put, straddle, flag.
Strike column: round spot at the entry time to the strike step, then copy that one value down every row for a fixed-ATM series. For a rolling series, use the rounding formula in each row.
Call and put columns: look up the price for that strike and time from your raw data tab, using both the strike and the timestamp as keys.
Straddle column: call plus put, but only if both cells are numbers.
Flag column: a simple condition comparing each straddle with the one above it, using your jump threshold.
A summary tab: for each day, entry premium, premium at selected times, the day's high and low, and the share of cells that were filled.
The spreadsheet approach is slow once you pass a few dozen days, and lookup formulas over large raw data can make the file heavy. It is a good way to learn, and a good way to check a Python script on a single day.

A short Python version
For a year of data, a script is faster and less error-prone. Describe it as a small pipeline.

Load the raw one-minute data into a table with timestamp, expiry, strike, option type, bid, ask, last.
Pick the weekly expiry for each date, normally the nearest expiry on or after that day.
Compute the mid price where bid and ask are both present, otherwise fall back to last traded.
For each day, read spot at the entry minute, round to the strike step, and keep that strike (fixed) or recompute each minute (rolling).
Reshape so each minute has a call and a put value, add them, and forward-fill up to your gap limit with a flag.
Run the jump, reversal and bound checks. Write flagged rows to a log.
Save one tidy file per day and a summary table across days.
Keep the script short and keep each step visible. If a step hides logic inside a clever one-liner, you will not remember what it did next quarter.

Checking That Your Series Is Right
Before you trust the output, test it. Three tests are worth running every time.

Pick two or three days you remember well, such as a big gap day or a quiet expiry, and see whether the shape matches your memory. A gap-down open should show as a jump in the fixed-strike straddle at the start, and a quiet pinned day should show a steady decline.

Compare your entry premium with the premium shown in your trading terminal for the same strike at the same minute. Small differences are fine. Large ones mean a timestamp or strike error.

On expiry days, check the end of the series. A fixed-strike straddle at the close should be close to the absolute distance between the closing level and the strike. If your series ends far from that, something is wrong in the last hour.

EliteAlgo's desk study also looked at where expiry closes relative to the max-pain strike. Across 163 sessions, the median distance was 34.4 points, or 0.083% of spot, which is a handy check number when you test your late-day values: a series that implies a close hundreds of points from any sensible strike on a quiet day is probably corrupted.

Reading the Line Once You Have It
A line you built is worth only what you can read from it. What follows is the minimum you need to start.

Entry premium as a share of spot
Divide the entry premium by spot to compare days. In the desk study, the 09:20 ATM straddle averaged 0.38% of spot when India VIX was under 12 and 0.83% of spot when VIX was above 20. The premium roughly doubled across the five VIX bands, rising in order in every band: 0.38%, 0.47%, 0.58%, 0.64% and 0.83% as VIX moved up. If your own series does not show that kind of ordering when you group by VIX, check your data before you check the market.

Path, not just endpoints
The most useful thing about a minute-level series is that it shows the path. A short straddle that ends the day in profit may have been far more profitable at midday. In the same study of short ATM straddles entered at 09:20, 86 of 159 usable days gave back more than 30% of their peak profit, and 48 of the 159 closed at a loss after having been in profit at some point. Those figures are descriptive, taken from one entry rule and one period. They are not a forecast. They do show why you want the full series, not just the open and close.

Common Mistakes When Building the Series
Most problems come from a short list. Mixing expiries is the most frequent. Next is time misalignment between spot and options. Third is using last price on thin strikes. Fourth is rolling the strike without recording which strike was used. Fifth is deleting outliers by hand without a log. Sixth is cutting the series at different times on different days, so averages do not line up.

A less obvious one is survivorship in your own data. If you build the series only for days when data was clean, you may drop exactly the fast days that matter most, because fast days produce gaps and bad ticks. Count how many days you dropped and why. If the dropped days cluster on high-volatility sessions, your averages will look calmer than reality.

The last is overfitting your cleaning rules. If you tune the jump threshold until your favourite strategy looks good, you have taught the data what you want to see. Set the thresholds from the data's own noise level, write them down, and leave them alone.

Frequently Asked Questions
What is an ATM straddle premium?

It is the combined price of the at-the-money call and put for the same strike and expiry. It shows roughly how much the market expects the index to move by expiry, and it is the premium a short-straddle seller collects.

Where can I get one-minute option-chain data for NIFTY and SENSEX?

Your broker's historical data feed, an exchange data product or a paid data vendor are the usual routes. Check the terms and the cost, and test a sample day before buying a long history. Make sure it includes expired contracts, because many free feeds drop them.

Should I use a fixed strike or a rolling ATM strike?

Use fixed if you want to see how a position you entered behaves through the day. Use rolling if you want to compare volatility levels across minutes or days. Many traders build both and label them clearly.

How do I handle missing minutes in option data?

Forward-fill a few minutes at most, flag every filled cell, and mark longer gaps as bad data. Do not fill with zero, and do not interpolate over long gaps. Report the share of filled cells for each day.

Can I build this in Google Sheets without coding?

Yes, for a small number of days. Use one tab per day and lookup formulas keyed on both strike and time. It becomes slow with many days, so move to a script when you pass a few weeks of data.

Conclusion
An ATM straddle premium series is simple to define and easy to get subtly wrong. The strike rule, the fixed or rolling choice, the gap policy and the bad-tick checks each shape the line you see. If you write down these choices before you look at results, keep the raw data untouched and log every fix, your series will be one you can trust.

Build it for a handful of days first. Compare it with what you remember and with your terminal. Then scale up. The result is a tool you understand end to end, which is worth more than any ready-made chart you cannot inspect.

Risk note: trading index options, and selling them in particular, carries a real risk of loss, including loss greater than your initial margin. EliteAlgo is analytical software and not an investment adviser. Nothing in this article is investment advice, a recommendation or a trade call, and figures marked illustrative are examples only.

How to Brief an Algo Trading Desk: The One-Page Document That Makes the First Call Useful

Rahul Goel — Tue, 10 Nov 2026 05:27:36 +0000

Most first calls with an algo desk or provider go the same way. The trader says he wants "a good algo for option selling". The desk asks a string of questions. He answers some of them from memory, guesses at others, and says he will get back on the rest. An hour later both sides know a little more and nothing has been decided.

That hour can be cut to fifteen minutes. The fix is a single page, written before the call, that answers the questions the desk is going to ask anyway. It also helps you, because writing it forces you to settle things you may have been avoiding, like how much you can lose in a day without changing your behaviour.

This article sets out what belongs on that page, line by line, with the reasoning for each. It uses real data from a study of NIFTY and SENSEX weekly expiry sessions to show how some of the numbers should be framed, and it ends with a sample brief you can copy as a layout. It applies whether you are speaking to a software vendor, a managed-account provider or a proprietary desk.

Why most first calls waste an hour
A desk cannot give you a useful answer to a vague question. "What returns can I expect?" has no answer until the desk knows your capital, your tolerance for a bad week, and what you will do when the strategy is losing. So the first part of any serious conversation is about you, not about the product.

The usual failure is not that the trader hides information. It is that he has never written it down, so each answer is a fresh guess. On Monday his daily loss limit is Rs 5,000. By Thursday it is Rs 15,000, because the first number was an estimate and the second is what he feels after a bad session. A desk that hears both numbers cannot tell which one to design for.

Writing the brief fixes this. It does three things.

It forces consistency. Once a number is on the page, you either stand by it or change it deliberately. It shortens the call, because the desk can read the page in two minutes and spend the call on the interesting parts. And it protects you, because a desk that proposes something outside your page has to explain why. If the proposal fits your page, you have a clear starting point. If it does not, you have an early warning.

The one page, at a glance
Here is the layout. Each row is expanded in later sections. The right-hand column shows what a filled-in line could look like. All values are illustrative and invented for the layout; they are not recommendations.

Where to send the brief, and who should read it
A brief is only useful if it reaches someone who can act on it. That sounds obvious, but a lot of traders send it to a general sales address and get back a brochure. The questions to settle first are who reads it and whether they trade or sell.

Look for a way to reach the desk directly. A good sign is a contact route that names the type of enquiry, shows a location and a phone or email, and does not force you through a chat widget first. A bad sign is a form that asks only for your phone number and nothing about what you want. If a firm is set up as a proprietary or quantitative desk, the person who answers should be able to talk about execution and risk, not just plans.

If you are looking for a desk based in the capital region and want a direct route, you can contact the EliteAlgo team in New Delhi through the firm's own contact page, and paste the one-pager into your message. Whichever desk you write to, do two checks before you send anything personal. Confirm the legal entity you are writing to, and confirm what it does and does not do. EliteAlgo, for instance, describes itself as a New Delhi proprietary and quantitative trading firm that provides analytical software. It is not an investment adviser. Reading that kind of description first tells you whether your brief is going to the right kind of firm.

Two more practical points. Send the brief as a PDF or plain text in the body of the email, and keep it free of identifiers you do not need to share. Your name and a contact number are enough. Do not send account numbers, API keys, PAN or screenshots of your holdings. A desk does not need those for a first conversation, and one that asks for them early is a flag.

Second, ask for a reply in writing to the key rows. A phone call is fine for discussion, but anything that affects your money should end up as a written line from the desk: what they would do, with what limits, and what they would not do.

Capital: three numbers, not one
The first row looks easy. "I have Rs 10 lakh." But a single capital number hides the three things a desk actually needs.

The first is the amount dedicated to the strategy. This is the money you will let the system use. It is not your net worth and not what is sitting in your broker account. Keep it separate in your own books, ideally in its own account, so a bad month cannot reach money you need.

The second is the margin that the strategy needs. For short option positions the broker blocks margin, and it changes with volatility and with the rules in force. Use your broker's current margin calculator for the structure you have in mind, and ask the desk to do the same. Do not rely on a number remembered from last year. The point of writing it down is to see how many lots your capital can carry with room to spare, since a position that uses nearly all your margin has no room to survive a spike.

The third is the floor. This is the account value at which you stop and review, before the strategy continues. It is the line in the sand. Most people skip it, then drift past what they can afford. A floor turns a vague fear into a rule.

Instruments and structures: be specific, and say what is out
"Option selling" is a category, not a specification. A desk needs to know which index, which expiry, and which structure.

On the index, state NIFTY, SENSEX or both. These are different contracts with different lot sizes, strike spacing and liquidity. NIFTY's lot size is 65. For SENSEX and for anything else, check the exchange's current contract specifications rather than trusting memory. Say which weekly expiry you want to trade. Weekly expiries exist for NIFTY on the NSE and SENSEX on the BSE. Bank Nifty weekly expiries were discontinued after the regulator's one-weekly-expiry-per-exchange rule, so do not ask a desk for them. A desk that says it will trade them is behind the times, or is not being straight with you.

On structure, name what you want in: short straddle, short strangle, iron condor, or a hedged version. And be just as clear about what is out. "No naked positions through the close" is a one-line instruction that changes the whole design. So is "no averaging down on a losing leg". These are the lines that save you money, because they stop a clever idea from becoming a dangerous one.

Last, say how you think about the premium. The size of the premium you can collect depends mostly on India VIX. In a study of 163 NIFTY and SENSEX weekly expiry sessions between January 2025 and July 2026, the 09:20 ATM straddle premium averaged 0.38% of spot when VIX was under 12, and 0.83% when it was over 20, with a steady rise across the bands in between. That is a doubling of the premium budget from the calmest to the most stressed. If your brief says "I want to collect about this much per day", the desk will rightly point out that the budget moves with VIX. A better line is "I want the structure sized so that a stop costs no more than X, in whatever VIX band applies."

Risk limits: write them as rupees, then as rules
This is the row that matters most, and the one most often left vague. "Low risk" is not a limit. A limit is a number in rupees, with a time frame, and a stated action when it is reached.

Write three limits. A daily loss limit: when the day's loss reaches this, everything is closed and no new entries are made until tomorrow. A weekly loss limit: when reached, the system is paused until you and the desk have talked. And a per-position stop: the rule that closes a single position, expressed in a form the desk can code, such as a rise in combined premium by a stated multiple, or a spot move by a stated percentage.

Here is an illustrative piece of working to show how these connect. Suppose you dedicate Rs 10 lakh and decide that one bad day should cost no more than 1%, which is Rs 10,000. You plan to sell a NIFTY straddle with a combined credit of 100 points and to exit if the combined premium rises 30%, to 130. That is a 30-point loss per unit at the stop. With a lot size of 65, one lot loses 30 x 65 = Rs 1,950 at the stop. Rs 10,000 divided by Rs 1,950 is about 5.1, so on paper, five lots fit inside the daily limit.

Now stress that working. It assumes the stop fills exactly at 130. In a fast market it may fill at 140 or worse, which changes the loss to 40 x 65 = Rs 2,600 per lot, and five lots lose Rs 13,000. It also ignores costs. So a careful trader would cut the size to four lots, or widen the daily limit on purpose. The point is not the exact answer. It is that a written daily limit, a stop rule and a lot size can be checked against each other in a minute, and a desk that sees them can tell you where they do not fit.

Add one more line: what happens to the limit on a gap or a halt. A stop is not a guarantee. If trading is stopped or the market jumps through your level, the loss can be larger than planned. Say so on the page, and say what you expect the desk to do about it, whether that is smaller size, a hedge, or avoiding certain days.

Finally, say what you measure success against. The same study looked at a short ATM straddle entered at 09:20 and found that the median give-back from peak profit to the close was 42.8%, and that on 48 of 159 usable days (30%) a position that had been in profit at some point ended the day at a loss. That means "end-of-day profit" and "peak profit" are very different yardsticks. If you want the desk to manage for end-of-day results, say so. If you want it to take profit on a trigger before the close, say so. A desk cannot read your mind on this, and the choice changes the whole design.

Broker, permissions and hours: say who holds the keys
The fifth row is where many briefs are thinnest and where a short paragraph does the most good.

Name your broker and your account type. A desk needs to know whether its tools connect to that broker at all. Not every platform supports every broker, and a good desk will say so early rather than discover it after you have signed.

State where orders are to be placed. The cleanest arrangement is your own broker account, in your own client code, so every order shows in your own app and you can always see the truth. If the desk proposes anything else, such as a pooled arrangement or an account in its name, you should hear that in the first call and ask about the legal status of the entity before going further. The regulator's website, sebi.gov.in, is the place to check what a given entity is registered for.

Hours, and what happens outside them
Markets are open for a fixed window, but your system and your attention have their own hours. State both.

Give the window in which entries are allowed. Many expiry-day sellers enter early, and the study above anchored its straddle at 09:20 for a reason: the early session is when the premium is richest and when most of the day's decay and re-pricing are still to come. The same study found that the average ATM straddle premium fell from 100% of its 09:16 level to about 88.6% by 11:16, then moved sideways between roughly 88% and 93% for the rest of the afternoon, because the strike is fixed while spot keeps walking. So if you plan entries late in the day, tell the desk, since the premium behaves differently then.

Give the time by which everything must be flat. That could be 15:15, 15:20, or a time before the close that suits your own nerves. And state what should happen to any position still open at that time: closed at market, or closed by a limit with a fallback.

Then cover the less obvious hours. When will you be reachable? If the desk needs a decision at 11:30 on an expiry day, will you pick up? If not, who can decide in your place? The answer "I will be in a meeting from 11 to 1 and cannot be reached" is perfectly fine, as long as the desk knows it and the brief says what the system does in that case, such as pause or reduce.

What to ask back, and what a good reply looks like
A brief works in both directions. Once the desk has your page, you have the right to a clear reply. Here is what to look for.

A good desk replies in its own words to each row. It tells you which parts of your page it can meet exactly, which parts need changing and why, and which parts it will not do. It answers the risk row with numbers. It is open about costs, including brokerage, exchange charges and taxes, and tells you how they affect a result that looks fine before costs.

It tells you what history it relied on. If it shows a backtest, ask for the period, the number of sessions, the entry and exit rules, the slippage assumed, and how it handled days when data was missing. A result without those details is a picture, not evidence. Sample size matters too. In the study referred to here, there were 163 sessions across two indexes, about 80 for each. That is enough to see broad patterns and not enough to say anything firm about a narrow slice such as one VIX band with eleven days in it.

It says what could go wrong, in specific terms. Not "markets carry risk", but "on a gap through the stop, the loss can exceed the planned amount, and here is how large it was on the worst day in our test". In the study the distance between the expiry close and the max-pain strike had a worst case of 415.4 points, or 0.81% of spot, against a median of 0.083%. The tail is far from the middle, and a good desk knows its own tail.

It does not promise a return. Not a fixed monthly number, not "safe", not "guaranteed". Any of these is a reason to stop.

A few patterns should slow you down. A desk that wants your login credentials rather than a scoped key. A desk that cannot say which legal entity it is. A desk that dismisses your daily loss limit as "too tight" without showing the maths. A desk that pushes you to start before you have seen the written reply. A desk that rewrites your brief into its own template and drops the rows you cared about.

None of these proves bad faith on its own. Together, or when the desk is evasive when you ask about them, they tell you enough.

A sample one-page brief you can copy as a layout
This sample is invented and illustrative. The numbers are not advice for anyone. Change every line to match your own situation.

Purpose. Looking for a systematic approach to weekly-expiry index option selling, with me approving changes.

Capital. Rs 10 lakh dedicated. Margin usage not to exceed 60% at entry. Floor: Rs 8 lakh, at which all activity pauses for review.

Instruments. NIFTY and SENSEX weekly index options. Short premium with defined stops. No overnight positions. No averaging down.

Risk limits. Daily loss limit Rs 10,000. Weekly Rs 25,000. Per-position stop as a premium multiple, to be proposed by the desk and approved by me in writing.

Broker. My own account with a mainstream broker. Scoped API access. I do the daily login. Stops rest at the broker where possible.

Hours. Entries between 09:20 and 10:30. Flat by 15:15. I am reachable by phone except 11:00 to 13:00.

Decision rights. Desk proposes, I approve. I can pause the system at any time. Manual overrides by the desk must be logged and flagged.

Reporting. Order-level log each evening. One-page summary each week.

My experience. Two years of manual expiry-day selling. Past losses came mostly from moving stops.

Exit. I revoke access at the broker. The desk deletes stored credentials and sends me the full log.

That is under 200 words, and it carries more information than most first calls. A desk that has this page can answer in specifics. You can compare two desks on the same page. And in six months, you can read it again and see whether you or the desk drifted.

Frequently Asked Questions
What should I prepare before contacting an algo trading company?

Write one page covering your dedicated capital, the instruments and structures you want, your daily and weekly loss limits in rupees, your broker and how access will be granted, your trading hours, and how you would exit the arrangement. It takes under an hour and saves the first call from becoming a questionnaire.

How much money do I need to start algo trading on NIFTY options?

It depends on the structure, the broker's current margin rules and how much buffer you want. Use your broker's margin calculator for the exact position, then add a buffer so you are not near the limit. Decide your floor and your daily loss limit first, and let those set the size.

Can I give an algo provider my broker login?

It is better not to. A scoped API key issued by the broker is narrower and can be revoked on its own, while a login and authenticator seed is full access. Ask the provider what credential it needs and why, and keep the daily login in your own hands if you can.

What loss limit should I tell an algo desk?

The one you can live with without changing your behaviour. Many traders choose a figure as a small percentage of dedicated capital, then test it against the stop rule and lot size, as in the worked example above. Gaps and fast markets can make a loss larger than the limit, so leave room.

How do I know if an algo desk is legitimate?

Ask for its exact legal entity name, what it is and is not registered for, and where you can verify that. Check the regulator's website. Be wary of any desk that promises returns, asks for your password, or cannot explain where your money and orders go.

Do I need a backtest before I speak to a desk?

No. You need your own limits first. A backtest is something you ask the desk to explain, with the period, the sample size, the entry and exit rules and the slippage assumed. A result with no such detail is not worth much.

Conclusion
A good first call starts before the call. One page, with capital, instruments, risk limits, broker, hours, decision rights and exit, lets a desk answer in specifics and lets you compare it fairly against the next one. The rows that matter most are the ones people skip: the floor, the loss limits in rupees, who holds the keys, and what happens outside market hours.

Write the page, keep it short, and keep it honest about your own habits. Then use it to test what comes back. If a desk's reply fits your page and explains its tail risks plainly, you have a basis to talk further. If it does not, you have lost fifteen minutes instead of an hour.

Algo trading carries a real risk of loss, and expiry-day option selling can lose more than the premium collected. EliteAlgo is analytical software and not an investment adviser, and nothing in this article is investment advice. All figures in worked examples and in the sample brief are illustrative.

NIFTY and Bank Nifty Algo Trading: Why a Strategy Built on Bank Nifty Weeklies Will Not Port Straight to NIFTY

Rahul Goel — Mon, 09 Nov 2026 06:02:34 +0000

Many Indian option sellers built their first strategy on Bank Nifty weekly expiries. For a few years that was the most active weekly contract in the country, and a lot of backtests, courses and shared templates were written around it. Those weekly contracts are gone. The surviving weekly expiries are NIFTY on the NSE and SENSEX on the BSE. So a large pool of old rules now needs a new home.

The tempting move is to open the strategy, change the symbol from Bank Nifty to NIFTY, and press run. The rules are the same, so the behaviour should be the same. It will not be. The entry time and the logic may carry over. The numbers inside the rules will not, and a few of those numbers can quietly double or halve your risk without a single line of the logic changing.

This article is not about the exchange rule change that retired the old contract. That story is well covered elsewhere. It is about the practical job of re-calibrating a strategy for a different index. I use a worked example with clearly labelled illustrative numbers, then a step-by-step procedure you can follow on your own rules.

What actually breaks when you copy the file
Think of a strategy as two layers. The first layer is the logic: sell a straddle at a set time, protect it with a hedge, exit on a stop or a time. The second layer is the parameters: how many points for the stop, how far away the hedge sits, how big the trailing step is, how many lots. When people say "the strategy works", they usually mean that the logic plus one particular set of parameters worked on one particular index.

Parameters are tuned to the thing they were tuned on. A 100-point stop makes sense on an index trading at 55,000 and makes a very different statement on an index trading at 25,000. Move the same number to the second index and you have not copied the strategy. You have created a new one, and nobody has tested it.

There are five places where a straight copy goes wrong, and they come up again below.

Any parameter written in index points.
Any parameter written in rupees, because the rupee value of a point depends on the lot size.
Strike distances, because strike spacing and index level differ.
Volatility-driven choices such as entry timing, stop width and filters.
Liquidity and slippage, which are different in percent terms on each index.
One more point before the detail. Every number I use for Bank Nifty below is an illustration. I am not stating its current level, lot size or premium. Exchanges revise lot sizes from time to time, and the contract's weekly expiry no longer exists. Treat those figures as a shape, and rebuild the calculation with your own data and the exchange's current contract specifications.

Three unit systems: points, percent and rupees
Every risk rule in an option-selling strategy is written in one of three units. Knowing which one you used is the first step in a port.

Points
A point is one index unit. "Stop at 80 points" means the premium moves 80 index units against you. Points are the easiest unit to type and the worst to port, because the same move in points means a different thing on a different index level.

Percent
Percent can mean percent of the premium collected ("stop at 25% of entry premium") or percent of spot ("hedge 0.9% away"). Percent-of-premium rules scale on their own with the volatility that the market is pricing. Percent-of-spot rules scale with index level. Both port much better than points.

Rupees
Rupees are what you actually lose. The conversion is points x lot size. For NIFTY, the lot size is 65, so one point on one lot is Rs 65. A rule such as "exit if the day's loss crosses Rs 5,000 per lot" is portable in the sense that it means the same thing for your account, but the point-move that triggers it differs. On NIFTY at 65 units, Rs 5,000 is about 77 points of loss on a single lot. On an index with a smaller lot, the same rupee figure is a larger point move.

A simple discipline follows. Rewrite each risk rule in all three units. If the three versions tell different stories on the new index, you have found the parameter that needs work.

A worked example: the same rules on two indices
Here is the illustration. All figures below are made up for the purpose of showing the mechanics. They are not market data, and I do not claim they are current. Imagine a strategy that sells an at-the-money straddle at 09:20 and has these rules, written in the way many Bank Nifty strategies were written, in points.

Stop-loss: exit the combined position if the straddle price rises by 110 points.
Hedge: buy a protective call and put 500 points away from the money.
Trailing step: tighten the stop by 20 points for every 40 points of profit.
Now compare the two indices under illustrative conditions: an index near 55,000 with a straddle priced at 0.80% of spot and a lot of 30 units, against NIFTY near 25,000 with a straddle priced at 0.47% of spot and a lot of 65 units. The 0.47% is the mean 09:20 straddle premium from EliteAlgo's desk study of 163 NIFTY and SENSEX expiry sessions for the VIX 12 to 14 band. The 0.80% for the other index is purely illustrative.

The stop becomes almost meaningless. On the first index, a 110-point stop is a quarter of the premium collected. On NIFTY, the same 110 points is 94% of the premium. The stop would fire only after nearly the whole premium has been lost, which is a very different risk profile from the one you tested.

The rupee risk more than doubles. A 110-point loss costs Rs 3,300 per lot on the first index and Rs 7,150 per lot on NIFTY, because 65 is more than twice the illustrative 30. So the larger lot and the unchanged point stop work together to inflate your loss per trade, even though the strategy "looks the same".

The hedge moves far away. A 500-point hedge was 0.91% of spot on one index and is 2.0% of spot on NIFTY. A hedge that far out costs very little and protects against only a violent move. It is cheaper, but it no longer does the job the original hedge did.

Now rewrite the rules in a form that travels. Express the stop as 25% of the entry premium, which on NIFTY at 117.5 points is about 29 points, or about Rs 1,909 per lot. Express the hedge as 0.9% of spot, which on NIFTY at 25,000 is 225 points; since NIFTY strikes are 50 points apart, round to 250 or 200. Express the trailing step as a fraction of premium, not points: the original 20 points per 40 points of profit was about 4.5% and 9% of a 440-point premium, which is roughly 5 and 10 points on a 117.5-point premium.

Rule Naive copy (NIFTY) Rescaled (NIFTY)
Stop 110 points (Rs 7,150 per lot) About 29 points (Rs 1,909 per lot)
Hedge 500 points away About 225 points, rounded to a strike
Trail step 20 points per 40 About 5 points per 10
Even this rescaled set is a starting hypothesis and not a finished strategy. It keeps the proportions. It does not prove that the proportions are right for NIFTY. The next section explains why the choice of proportion needs its own evidence.

Volatility scaling: measure it, do not assume it
Proportional rescaling assumes that the two indices behave like scaled copies of each other. They do not, and the ratio is not a constant. Bank Nifty is a sector index made of banking and financial stocks. NIFTY is a broad index of 50 companies across sectors. The narrower index has tended to swing more in percent terms, and it reacts to different news, such as rate decisions and bank results. How much more, and on which days, changes over time. Do not take a rule of thumb from a forum. Measure it from your own data.

You can do this with a short list of statistics, calculated on matching sessions from both indices.

The average and median of the day's high-to-low range as a percent of the open.
The average and median of the move from your entry time to the close, in percent.
The average premium at your entry time, as a percent of spot, split by India VIX band.
How often the index moves more than your stop distance in percent terms, within your trade window.
The third item deserves special attention, because the premium itself is the market's own estimate of the expected move. In the desk study, the 09:20 straddle premium on NIFTY and SENSEX rose in a clean line as India VIX rose: 0.38% of spot under VIX 12, 0.47% for 12 to 14, 0.58% for 14 to 17, 0.64% for 17 to 20 and 0.83% above 20. The budget roughly doubles from the calmest band to the most stressed. If you size stops as a fraction of premium, the stop widens when the market expects more movement and tightens when it does not. That is a feature, and it is the reason percent-of-premium rules port better than point rules. But it is only a feature if the strategy was designed around it. A rule that was tuned to a fixed point stop might behave worse once it floats with premium.

If you want to see how a published framework treats the two main index contracts side by side, EliteAlgo has a page on NIFTY and Bank Nifty algo trading. That is useful for context. For the porting job itself, the evidence you need is your own measured table, built from your own sessions.

Hedges, strikes and spreads: the liquidity side
Parameters in points are only part of the porting job. The market itself is built differently on each index, and that touches your hedge choice and your costs.

Strike spacing
NIFTY options near the money are listed 50 points apart. At an index level of 25,000, one step is 0.2% of spot. If your old rule said "hedge 5 strikes away" on an index with 100-point steps, that was 500 points. On NIFTY the same five strikes is only 250 points. So decide whether your rule is about strikes, points or percent, and convert on purpose.

Hedge cost and what it buys
The further out the hedge, the cheaper it is and the less it protects. The right distance depends on the worst move you are trying to survive. A useful method is to look at the largest single-day moves in your sample, in percent, and ask what your account would lose with the hedge at various distances. Do this for NIFTY directly. Do not carry over the distance from another index.

Bid-ask spread in percent of premium
Look at the spread as a share of the premium you are trading. A spread of 0.30 points on a 117-point straddle is small. The same 0.30 points on a 10-point hedge leg is 3% of its price. The hedge legs are where percentage costs bite. When you port, record the actual spreads at your entry and exit times on NIFTY for each leg, and put them into your backtest as a cost per leg. Do not reuse the slippage assumption from the other index.

Depth and order size
Check open interest and traded volume at the strikes you plan to use, at the times you plan to trade. Thin strikes give wide spreads. Exchanges also cap the quantity per order, so large lot counts must be sliced, and these limits change over time. Read the current contract specifications on nseindia.com and test your slicing logic.

The re-calibration procedure, step by step
Here is the sequence I would follow to move a Bank Nifty weekly strategy to NIFTY. It is deliberately dull. Each step produces a record you can review later.

Step 1. Write the rules in plain words, with units. List every parameter and mark whether it is in points, percent of premium, percent of spot, strikes, or rupees. A rule you cannot express in one of these is vague and needs to be fixed first.

Step 2. Convert each parameter to a dimensionless form where possible. Stops and trails become percent of entry premium. Hedges become percent of spot. Timing stays in clock time for now.

Step 3. Map to NIFTY contract details. Lot size 65 (confirm the current value with the exchange), strike spacing, expiry day, trading hours and freeze quantity.

Step 4. Build the NIFTY premium table. Calculate the 09:20 straddle premium as a percent of spot by India VIX band, for as many weekly sessions as you can get. Compare to the table above and ask whether your data shows the same shape.

Step 5. Re-test the timing. The entry time that worked on the old index may not be the best on NIFTY. Desk data shows the average NIFTY and SENSEX straddle premium fell from 100% of its 09:16 value to about 88.6% by 11:16, and then moved sideways in an 88% to 93% band for the rest of the day. That shape came from a fixed strike with a moving spot, which nets time decay against re-pricing, and it is not a clean decay curve. If your old strategy assumed a late-day collapse in premium, check whether it exists in your NIFTY sample.

Step 6. Fit the stop on NIFTY's own path. Use a grid of stop sizes as percent of premium (for example 15%, 20%, 25%, 30%, 40%). For each, record the number of stop-outs, the average loss on stopped days and the average result on the others. Choose a width where the result is stable across neighbouring values. If the result swings sharply from 25% to 30%, you have fitted noise.

Step 7. Re-size in rupees. Decide the maximum loss per trade and per day you accept, in rupees, from your account size. Work backwards to the number of lots. Do not carry over the lot count from the other index.

Step 8. Replace the cost assumptions. Use NIFTY spreads, brokerage and taxes. Add a slippage figure measured from small live or paper orders.

Step 9. Run golden days. Pick five to ten sessions, including a quiet day, a gap, a trend and a sharp reversal. Write down by hand what the strategy should do on each. Run the code. Compare line by line.

Step 10. Paper trade, then trade one lot. At 65 units a single lot is already a real amount of money. Run at least several weeks on paper, then one lot, and keep a log of every difference between expected and actual behaviour.

Step Output you should have
1-2 A one-page rule sheet with units
3 A list of contract facts and the date you checked them
4 A premium-by-VIX-band table for NIFTY
5 A chart or table of premium by time of day
6 A stop-width grid with results
7 A rupee risk limit and lot count
8 A cost sheet per leg
9 Golden-day comparisons
10 A trade log with expected-versus-actual notes
Even NIFTY and SENSEX are not copies of each other
If you are tempted to think that two indices that follow the same rules must behave alike, look at what happens between the two surviving weekly contracts. NIFTY trades on the NSE and SENSEX on the BSE. Both are large-cap benchmarks, both have weekly expiries, and yet the desk study found clear differences in one measure.

On expiry day, the study measured how far the closing level sat from the max-pain strike, which is the strike at which option buyers collectively lose the most. Across the 163 sessions, the median distance was about 0.083% of spot. But split by index, NIFTY's median was 0.130% of spot while SENSEX's was 0.060%. The mean told the same story: 0.160% for NIFTY and 0.083% for SENSEX. SENSEX closed about half as far from max pain as NIFTY did.

You do not need to believe that max pain is a tradable signal to see the lesson. Two index options on the same type of expiry, in the same period, gave a measurably different closing behaviour. A strategy that leans on pinning near a strike (for instance, a tight short strangle held to the close) would have a different hit rate on each. If that is true for two close relatives, it is even more true for a sector index and a broad index. So ask of every part of your strategy: which of my assumptions come from the old index's own behaviour?

Max-pain distance at expiry close NIFTY (83 sessions) SENSEX (80 sessions)
Mean, % of spot 0.160% 0.083%
Median, % of spot 0.130% 0.060%
Another number from the same study applies to anyone who ports a strategy and worries about margin. Using a simple proxy for exposure, where notional is spot times one plus 0.15% of the day's VIX, the peak exposure ran only about 0.7% above entry exposure on average, with a maximum of 2.5%. This is a proxy and not live SPAN margin, so your broker's margin screen is what governs. It does suggest that for a fixed-strike short straddle, exposure does not balloon during the day on its own. The real swing in your margin and risk comes from the stop, the hedge and the sizing rules you set, and those are exactly the items a port changes.

What history you can and cannot trust
The old strategy's evidence is the backtest on Bank Nifty weekly data. That evidence has limits that become sharper after a port.

First, the history is closed. The weekly contract no longer trades, so you cannot compare live results to the backtest going forward. The record is fixed, and anything you learned from it is an input to a hypothesis about NIFTY, not a proof.

Second, the regime may have been special. A strategy that sold weeklies in a particular stretch of years may have benefited from the conditions then, such as the volume of retail buyers or the level of volatility. Do not assume the conditions carry over. If you can, look at how the old results varied by VIX band, and see whether NIFTY is currently in a similar band.

Third, you now have a smaller data set on the new index at the same frequency. The desk study uses 163 sessions across both indices, which is useful but not huge, and the sample splits into 83 for NIFTY. Treat any NIFTY-only conclusion drawn from a few dozen weeks as tentative. Prefer rules that are stable across neighbouring values, and size small until the live record builds up.

Fourth, do not tune the NIFTY version until its backtest looks like the old one. If the honest version is weaker, that is information.

Frequently Asked Questions
Can I use my Bank Nifty weekly strategy on NIFTY without changes?

You can run it, but you should not trust it. Point-based stops, hedge distances and lot counts change meaning on a different index. Rewrite the rules in percent of premium and percent of spot, re-test on NIFTY data and re-size in rupees before using real money.

What is the NIFTY lot size and why does it matter for a port?

The NIFTY lot size is 65 at the time of writing. Exchanges revise lot sizes from time to time, so check the current value. It matters because the rupee value of one point equals the lot size, so a stop written in points costs a different amount on each index.

Is Bank Nifty weekly expiry still available?

No. After the exchange rule limiting each exchange to one weekly expiry, the weekly Bank Nifty contract was discontinued. The surviving weekly index contracts are NIFTY on the NSE and SENSEX on the BSE. You can check current contract details on the exchange websites.

Should I use points or percent for my stop-loss?

Percent of the entry premium ports best, because it floats with the volatility that the market prices in. Points are fixed and ignore the index level and the VIX. A common approach is to test several percent widths and pick one where the results are stable across neighbours.

How many weeks should I paper trade after porting?

There is no magic number. A reasonable minimum is several weeks that include different volatility conditions, with a log of every gap between expected and actual behaviour. Then start with one lot. Judge by whether the execution matches your assumptions, not only by profit.

Does a higher India VIX mean a bigger stop is needed?

The premium collected rises with VIX, so a stop set as a share of premium widens on its own. In a study of 163 NIFTY and SENSEX expiry sessions, the 09:20 straddle premium averaged 0.38% of spot under VIX 12 and 0.83% over VIX 20. Whether a wider stop is better still needs testing on your own data.

Conclusion
A strategy is a set of rules with numbers in them, and the numbers belong to the market they were tuned on. Moving from Bank Nifty weeklies to NIFTY is a re-calibration job. Rewrite each rule in units that travel, measure NIFTY's own premium and movement, rebuild the stop and hedge from that evidence, and re-size in rupees at 65 units a lot.

Then test it the slow way. Golden days, paper trading, one lot, and a log. If the result is weaker than the old backtest, accept that. The old record belongs to a contract that no longer exists, and the new record has to be earned.

A last word on risk. Algo trading carries a real risk of loss, including the loss of the capital you deploy, and a careful port does not remove it. EliteAlgo is analytical software and not an investment adviser, and nothing in this article is investment advice. All worked figures here are illustrations to show the method.

One Minute Is a Long Time: What 1-Minute Option Data Hides in Stop-Loss and Target Tests

Rahul Goel — Fri, 06 Nov 2026 07:20:00 +0000

Most retail backtests of index option selling run on one-minute bars. Each bar gives four prices: open, high, low and close. From those four numbers the software decides whether your stop-loss was hit, whether your target was hit, and at what price you got out.

The trouble is that a minute is not an instant. In sixty seconds, a NIFTY or SENSEX weekly option on expiry day can trade up, down, and up again. The bar records the extremes but not the order. If your stop sits inside the bar's range and your target also sits inside it, the data cannot tell you which one came first. The software has to guess, and it usually guesses in a way that flatters you.

This article is about that guess. It explains where one-minute data is blind, shows with simple numbers when the blindness matters, and gives a method to put a fence around the error instead of ignoring it. It is written for someone who sells index options on rules and wants to know how much to trust a stop-loss test. It does not recommend any trade, and the example figures are illustrative unless stated otherwise.

What a bar tells you, and what it does not
A one-minute option bar usually comes from last traded prices. Open is the first trade in the minute. High is the highest trade. Low is the lowest. Close is the last. It does not tell you the order of trades in between, how many contracts changed hands at each level, or what the bid and ask were at any moment.

For a short option leg, the high is the adverse extreme, since a rising option price is a loss to the seller. The low is the favourable extreme. So the high tests your stop, and the low tests your target. A simple engine checks: if high is at or above the stop price, the stop fired. If low is at or below the target, the target fired.

That works when only one of them is touched inside a bar. It breaks when both are touched in the same bar. And it hides three further things: the price you actually get at the stop, the effect of the spread, and the possibility that the high was a stray trade. The next sections take each one.

Both levels inside one bar: the ordering problem
Take an illustrative short ATM call sold at 60 points. You use a tight rule: stop at 66 (10% above entry) and target at 54 (10% below). The next one-minute bar prints open 60, high 67, low 53, close 61.

Both levels were touched. Perhaps the price fell to 53 first, hit your target and you banked 6 points. Or it spiked to 67 first, hit your stop and you lost 6 points. Same bar, opposite results, a swing of 12 points on a single trade, which is Rs 780 on one lot of 65.

Different engines resolve this in different ways, and the choice is rarely shown on the report. Three common rules exist.

The first is "target first". The engine checks the favourable level before the adverse one. This is the optimistic rule. It turns every ambiguous bar into a win.

The second is "stop first". The engine checks the adverse level first. This is the pessimistic rule. It turns every ambiguous bar into a loss.

The third is "open-distance". The engine guesses that the price visited whichever level was closer to the open first. In the example the open is 60, the stop is 6 away and the target is 6 away, so it is a tie, and the engine needs a tie-break anyway. Some engines use the candle colour: if close is above open, assume the low came first, then the high. This has some intuition behind it, but little evidence, and it is still a guess.

No rule is correct. Each is an assumption. What matters is how many trades fall into the ambiguous group, because only those trades depend on the assumption.

Ambiguity gets worse as the day goes on
Here is a point that is rarely mentioned. The share of ambiguous bars is not constant through the day. It depends on how big the bar's range is relative to the distance to your stop and target, and both of those change as premium decays.

When you express stops and targets as a percentage of premium, the rupee distance shrinks as premium falls. A 10% band around a 60-point option is 6 points each way. The same 10% around a 12-point option is 1.2 points each way. But the typical size of a one-minute range does not shrink in proportion. Option prices move in ticks of 0.05, spreads do not shrink to zero, and spot continues to move. So a one-minute range of 2 points is 3% of a 60-point option and 17% of a 12-point option.

The result: late in the session, a one-minute bar is much more likely to span both your stop and your target. If you run percentage-based rules that also apply to afternoon entries or re-entries, the ambiguous share can be much higher than in the morning. A backtest that reports one overall number hides this. A backtest split by time of day shows it.

The desk data helps frame the scale. EliteAlgo's study of 163 NIFTY and SENSEX weekly expiry sessions between January 2025 and July 2026 measured the ATM straddle premium at 15-minute marks, as a percentage of its 09:16 level. It reads 95.4% at 09:31, 89.0% at 11:01, and between 88% and 93% for the whole afternoon (for example, 89.1% at 13:01 and 90.9% at 15:01). The strike is fixed at the 09:20 level, so the straddle premium does not fall to near zero on average, because theta decay is offset by gamma. That means the average straddle stays a meaningful size all day. But individual legs behave differently: when spot trends, one leg decays to small values while the other grows. The small leg is exactly where the percentage-based ambiguity is worst.

The trigger price is not the fill price
The second blind spot is the price you receive. Most simple engines fill a stop at the stop price itself. Real stops do not work like that.

Consider the two kinds of stop order. A stop-market order becomes a market order when the trigger is hit, so you get whatever price the book offers at that moment, which in a fast move is worse than the trigger. A stop-limit order becomes a limit order, so you get at least your limit price but might not get filled at all if the price runs through. Neither gives you "the trigger price" reliably.

The gap case is the clearest. Suppose your stop is at 66 and the next bar opens at 71 because spot jumped between bars. A correct engine fills at the open, 71, not at 66. A lazy engine fills at 66 and pretends the gap did not happen. On expiry day, with fast spot moves and thin strikes, such gaps are common. Each one shifts the loss from the 6 points the rule planned to 11 points in this example, which is nearly double.

The fix is a simple fill rule for a short stop: fill price is the higher of the bar open and the trigger price, plus a slippage allowance for the part of the move that happened inside the bar. For a target, the opposite: fill at the lower of open and trigger, and do not give yourself extra for a favourable gap if you are using a limit order, since a resting limit order fills at your price (or better if the book allows) but you cannot count on a better price in a test.

Check also what the engine does with the entry. If you plan to enter at 09:20, does the engine use the open of the 09:20 bar, or the close of the 09:20 bar? The close of that bar is known only at 09:21. Using it as an entry price is a look-ahead error. It is a small one on average and a large one on the days when the first minute moves a lot.

Last-trade prices are not bid and ask
The third blind spot is the spread. A bar made from last-traded prices tells you where trades happened, not where you could have traded. A short seller sells at the bid and buys back at the ask. The gap between them is a cost you pay on each round trip, and it is missing from a bar-based test unless you add it.

For liquid at-the-money NIFTY weeklies the spread may be a small fraction of a point most of the time. For out-of-the-money hedge legs and for far strikes it can be much wider. During fast moves it widens for everyone. So the cost is not constant. It is lowest when nothing is happening and highest when your stop fires.

There is a related problem: stray prints. A high that is a single trade at an unusual price, say a small order hitting a thin ask, can trigger a stop in the test that no real stop order would have seen, since a real stop is usually keyed to the last traded price in the live market but your fill depends on the book. The reverse also happens: a real stop fires on a print that your bar's high smoothed over. For illiquid strikes, one-minute highs and lows are noisy.

When you read reviews of options backtesting software, the useful questions are the ones about these rules: which resolution rule for ambiguous bars, which fill rule for stops, and whether a spread allowance can be set. Feature lists usually skip them. Whatever tool you use, find out the answers from its own documentation before trusting a stop-loss result, and if the documentation is silent, treat the answer as unknown and test it yourself with the method below.

A quick way to size the effect on your own data: take a sample of 50 stop-outs from a test and look up the actual live fills for similar stop-outs from your own trading, if you have them. The difference between the planned loss and the fill loss, averaged, is your slippage on stops. Many traders find it is a substantial fraction of the planned stop distance, particularly when stops are tight.

Bounding the error: the three-run envelope
You cannot remove the uncertainty from one-minute data. You can measure how much it matters. The method is to run the same test three times with three sets of assumptions, and report the range instead of a single number.

Report the total result, the win rate and the drawdown for each run. The gap between the optimistic and pessimistic runs is the width of your uncertainty. If a strategy makes money in all three, the one-minute problem is not what is carrying the result. If it makes money in the optimistic run, breaks even in the middle and loses in the pessimistic one, the whole edge lives inside the assumptions, and you should not trade it on this evidence.

Here is an illustrative case. Suppose a rule set produces 200 trades over a test. Of those, 30 bars are ambiguous (15%). The average win on those trades is 6 points and the average loss is 6 points. Under target-first, all 30 are wins: +180 points. Under stop-first, all 30 are losses: -180 points. The swing between the two extremes is 360 points, or Rs 23,400 at 65 per lot. If the rest of the trades net +100 points in total, the strategy shows +280 points under the optimistic rule and -80 points under the pessimistic one. The same data and the same rules produce a winning and a losing strategy. That is the size of the thing you are hiding when you use a single rule without saying so.

Notice also that the ambiguous share, 15% in this example, is itself the most useful single statistic. If it is 2%, the problem is small and the three runs will look nearly identical. If it is 25%, your rules are too fine for one-minute data and no choice of assumption will rescue them. In that case you need finer data (tick data, or at least bid-ask snapshots) or looser rules.

How to count your ambiguous trades
This takes an hour for anyone comfortable with a spreadsheet or a few lines of code. For every trade in your test, at each bar after entry, check two conditions at once: did the high reach the stop, and did the low reach the target? Mark the bar as ambiguous if both are true. Also mark bars where the open already lies beyond the trigger, since those are gap fills and need their own fill price.

Then produce four numbers: the share of trades that ended on an ambiguous bar, the share that ended on a gap fill, the average points lost to gaps against the trigger price, and the same ambiguous share split by time of day in hourly buckets. The last one tells you whether your afternoon logic is built on guesses.

If you cannot get these numbers from your tool, export the one-minute data and compute them yourself. Most tools allow an export of the trade list but not the logic. If yours allows neither, that is itself a result: you cannot audit the test, so you cannot weigh it.

Another useful check is sensitivity to a one-minute shift. Re-run the test with entry delayed by one minute, and with every stop trigger set one tick wider. A robust result moves a little. A fragile one swings. A result that depends on a precise one-minute alignment is not telling you about the market.

What this means for studies built on 1-minute marks
The same caution applies to published statistics, including the ones used here. EliteAlgo's desk data on peak-to-close give-back for a short ATM straddle entered at 09:20 covers 159 usable sessions and finds a median give-back of 42.8% of peak profit, with 86 of 159 sessions (54%) giving back more than 30%, and 48 of 159 (about 30%) closing at a loss after showing a profit at some point. These are measured on one-minute option-chain data.

A one-minute mark can miss the true intraday peak. The best profit of the session may have occurred in the middle of a minute that closed lower. If so, the real peak is at least as high as the measured one, and the real give-back is at least as large. So the direction of the error probably works against the comfortable reading: the give-back is likely understated, not overstated. That is a modest claim and it rests on the plain fact that a bar's extremes lie outside its close. It does not turn these figures into forecasts, and the same data can say nothing about how tick-level exits would have performed.

The same logic applies to the max-pain figure: the median distance between expiry close and max-pain strike was 34.4 points (0.083% of spot) in that study, measured from an end-of-day open-interest snapshot. A snapshot is a point-in-time measure. It is a fair description of where the close sat relative to positioning, and it is silent on the path that got there.

The takeaway is not that one-minute data is bad. It is the best data many retail traders can get, and it supports many useful questions. The takeaway is that it supports some questions well (where did premium sit at 11:00?) and others poorly (which came first inside this minute?). Match the question to the resolution. Stop-loss and target ordering is the one most often asked of data that cannot answer it.

A short checklist before you trust a stop-loss backtest
Ask these questions of any test, your own or someone else's. How does it resolve bars where stop and target are both touched? What fill does it assume for a stop, and what happens on a gap through the trigger? Are spread and slippage included, and are they fixed or do they widen with speed? What share of trades end on ambiguous bars, and how does that share change by time of day? What happens to the result under the pessimistic run?

If the test can answer all five, it is worth reading. If it cannot, it is a rough sketch, which is fine as long as you do not put real money behind it.

Finally, check the regulator's and exchange's pages, such as nseindia.com, for contract specifications, tick sizes and trading hours. Tick size and expiry-day rules shape how coarse the price grid is, and a grid of 0.05 is coarse for a leg trading near 2 points, so a stop expressed as a percentage can land between ticks.

Frequently Asked Questions
Does a stop-loss work the same in a backtest as it does live?

Not exactly. A backtest on one-minute bars usually fills the stop at the trigger price, ignores the spread, and picks a side when stop and target fall in the same bar. Live stops fill at the available price, which is often worse in fast moves. Measure the gap on your own fills.

Which fires first, the stop-loss or the target, inside one candle?

One-minute data cannot say. The bar records the high and low but not their order. Run the test under both orders and report the range. If the result changes sign, the data cannot support the rule.

Is tick data necessary for options backtesting?

Not always. If your stops and targets are wide compared with a bar's typical range, one-minute data is fine. If they are tight, or you trade late in the day when premium is small, finer data or looser rules are safer.

How much slippage should I add to a stop-loss backtest?

There is no single figure. It depends on the strike, the time of day and the speed of the move. Start from your own live fills for similar stops, and use the pessimistic run to see how much the result depends on it.

Why does my live result differ from the backtest on expiry day?

The usual causes are fills at the stop that are worse than the trigger, spreads that widen in fast moves, gap openings between bars, and tie-breaks in ambiguous bars that favoured you. Count each one using the method above.

Should I use limit or market orders for stops on options?

Each has a trade-off. A market order is filled but at an uncertain price. A limit order has a price floor but may not fill when the price runs through it. For a short option, an unfilled stop is the bigger danger, so many traders accept market-style exits with a protection band. Check the exchange and broker rules for what order types are allowed on index options.

Conclusion
One-minute option bars hide the order of events inside each minute. When a stop and a target both sit inside one bar, the data cannot tell you which came first, and the software's silent tie-break can decide whether a strategy wins or loses. The problem gets bigger late in the day, when premium is small and a minute's range is a large share of it.

You can fence the error in. Count the ambiguous trades. Run optimistic, middle and pessimistic versions with fill and spread rules written down. Report the range. A strategy that survives the pessimistic run has earned some trust. One that does not has told you something useful before it cost you money.

Algo trading carries a real risk of loss, and a careful backtest does not remove it. EliteAlgo is analytical software and not an investment adviser, and nothing in this article is investment advice. The worked figures are illustrative, and the session statistics come from past data that does not predict future results.

The Price of a Backtest Changes the Way You Research

Rahul Goel — Thu, 05 Nov 2026 09:39:57 +0000

Ask ten option sellers how they choose a backtesting tool and nine will talk about data, speed or the strategy builder. Very few will say what actually happens in their routine: the pricing model decides how many tests they run, and the number of tests decides which strategies they end up trading.

This is not a complaint about any vendor. Every pricing model has a reason. The point is that a cost model is also a behaviour model. Pay per run and you will think before you click. Pay a flat fee and you will click far more often. Pay nothing and you will click until the tool stops you. Each habit leaves a mark on your results, and the mark is mostly invisible in the final equity curve.

This article looks at the three common models in plain terms: credits, flat subscription and free tiers. It explains how each one bends research, shows the arithmetic of why the best of many tests is usually flattering, and ends with a simple ledger you can keep so that your tool's pricing does not choose your strategy for you. It names no vendor prices and makes no claims about any product. Those details change, so check them in the vendor's own documents.

A backtest has a cost even when the tool is free
Start with the idea that every backtest costs something. Money is the obvious part. The less obvious parts are waiting time, setup effort and the attention you spend reading the result. A tool that charges nothing per run may still charge you in queue time or in the number of runs allowed per day. A tool that charges per run may be cheap in time, because it returns results fast.

Economists call this the marginal cost: the price of one more run. It is a better lens than the monthly bill. Two tools can cost the same in a month and yet have very different marginal costs, because one lets you run twenty tests and the other lets you run two thousand.

What matters for research is that marginal cost sets your default behaviour. When one more run costs nothing, you run it. When one more run costs real money, you run it only if you expect it to teach you something. Both are reasonable. Both have a failure mode. The next section takes them in turn.

Three pricing models, three habits
Credit-based pricing: the rationing habit
Under a credit model you buy a pool of runs, or a pool of compute, and each backtest draws it down. The visible effect is that you slow down. You write the hypothesis before you run it. You choose the date range carefully. You think about whether a test is worth the credits.

That is good discipline. A trader who has to ask "what will I learn from this run?" is doing what a researcher should do. Credits also make the cost of curiosity visible, which stops the common habit of running a hundred small variations to see what sticks.

The failure mode is the opposite one. Rationing makes you protective of the first idea. If a run costs something, you are less willing to test the version that might disprove your favourite setup. You run the test you expect to pass. You skip the awkward neighbours: the same entry with a different exit time, the same strategy on the other index, the same rule on only the high-VIX days. Credits also push people to test over short date ranges to save cost, and a short range is where luck is loudest.

A second failure mode is quieter. When credits run out near the end of a billing period, people use the last of them on a rushed test and accept the answer. The decision to go live is then made by the calendar, not by the evidence.

How to use credits well: spend the first part of your allowance on the checks that could kill your idea, not confirm it. Run the long date range first. Run the cost-heavy version first. If the idea survives both, you have earned the right to optimise.

Flat subscriptions: the over-testing habit
A flat monthly fee turns the marginal cost of a run to near zero. This is the best model for exploration, because you can try a wild idea without guilt. It is also the model most likely to produce a beautiful backtest of nothing.

The mechanism is simple and it is worth seeing in numbers. Suppose you test a strategy variant, and the result is just noise: no real edge, only luck. Let us measure each result on a standard scale where zero means no edge and one means one standard deviation of luck. If you run one such test, the expected result is zero. If you run ten independent noise tests and keep the best, the expected best is about 1.5 on that scale. Run a hundred and the expected best is about 2.5. Run a thousand and it is about 3.2. These are standard results for the maximum of independent random draws, and they are not specific to trading.

Variants tested (pure noise) Expected best result, in units of luck
1 0
10 about 1.5
100 about 2.5
1,000 about 3.2
Read the table again with a flat subscription in mind. A trader who sweeps stop-loss from 10% to 50% in steps, entry time across 30 candidates, and two target settings has already run 5 x 30 x 2 = 300 variants, taking five stop values. The best of those 300 will look like a strong result even if the strategy has no edge at all. The more freedom the tool gives, the higher the best-of-many number climbs, and the less it means.

The honest answer is to count your trials and discount accordingly. The count is the single most useful piece of metadata in your research, and almost nobody records it. A flat fee does not make this problem. It removes the brake that would have made you notice it.

There is a second hidden cost on a flat plan: attention. Cheap runs invite long sessions. After the fortieth run of an evening, you are no longer reading results. You are scanning for green numbers. Set a limit on runs per session yourself, because the tool will not.

Free tiers: the constraint you did not choose
A free plan has a price too, and it is paid in limits. Typical limits in tools of every kind are a cap on date range, on number of saved strategies, on runs per day, on legs, or on features such as exports. They differ from vendor to vendor and change over time, so verify the current ones in the vendor's own documents instead of trusting a forum post.

The research problem with limits is that they pick your design for you. If a free tier shortens the allowed history, you test on a short window. If it caps the number of legs, you test a simpler structure than you would trade. If it blocks exports, you cannot run your own analysis on the trade list, so you rely on the summary the tool shows. Each limit shapes what you can learn, and none of them is shown in the equity curve.

Free tiers are still worth using. They are good for learning a platform and for a first pass at an idea. The mistake is to take a free-tier result, made on a shorter window and a simpler structure, and treat it as the answer for the real strategy. A useful rule: write down every limit that applied to a test next to the result. "Tested on last 6 months, 2 legs only, no costs" is a very different sentence from "tested".

Comparing models by behaviour, not by price
When people search for AlgoTest alternatives, the usual comparison is a table of features and a line for monthly price. A better question to put next to it is: what will this pricing make me do in an ordinary month? That is the question the table below tries to answer in general terms. It applies to any tool in any category, and the rows describe tendencies, not facts about any named product.

Model Marginal cost of one run Natural habit Main research risk Counter-habit
Credits Visible, every run Few, planned tests Protecting the first idea; short test windows Spend first credits on tests that could kill the idea
Flat subscription Near zero Many tests, wide sweeps Best-of-many bias; fatigue Keep a trial ledger; cap runs per session
Free tier Zero money, limits on scope Test whatever fits the limits Results from a narrowed design Record the limits beside each result
Per-seat or team plan Shared, hidden Others run tests on the same data Nobody owns the trial count One shared ledger across users
None of these rows says one model is better. A flat plan can suit a careful researcher who keeps a ledger. A credit plan can suit a careless one who runs the same broken test five times. What the table shows is that the pricing model sets the default, and the default is what most people follow.

The trial ledger: the habit that fixes all three
The fix is not a better tool. It is a file. Open a spreadsheet or a plain text note for each strategy idea, and record every backtest you run on it. Five columns are enough: date, what changed from the last run, date range, cost assumptions, and result. Add a sixth if you like: why you ran it.

This does three jobs. First, it gives you the number of trials. When you finally pick a winner, you can say "this was the best of 64 variants", and you can judge it against the table above. Second, it shows you the path. You can see whether you changed one thing at a time or wandered. Third, it works against the urge to forget bad runs. Most people remember the runs that looked good and quietly drop the rest.

With a ledger in hand, you can apply a simple discount rule. For every tenfold increase in the number of variants you tried, expect the best result to look better by roughly one unit of luck or a little less in the noise case. If your winning result is not clearly above what pure noise would give for your trial count, treat it as a hypothesis and not a finding. This is a rough guide, not a formula for truth, but it prevents the worst errors.

Thin samples make the problem worse
The number of trials is one side of the problem. The other is how much data each trial rests on. Consider a real example of how fast slicing eats a sample. EliteAlgo's desk study of 163 NIFTY and SENSEX weekly expiry sessions between January 2025 and July 2026 is a decent base for broad questions, with 83 NIFTY and 80 SENSEX days. Now split those sessions by India VIX band, as the study did for its premium measurements: 46 days under 12, 43 in the 12 to 14 band, 35 in 14 to 17, 24 in 17 to 20 and just 11 above 20.

The top band has 11 days. Any strategy statistic computed on 11 expiry sessions is fragile. Add a second slice, say separating NIFTY from SENSEX, and a cell has perhaps 5 or 6 days. Add a third (weekday of entry, or a particular entry time) and you may be studying two or three days. In a flat-fee tool, nothing stops you from slicing this way. The tool will happily print a profit factor for three trades.

The link to pricing is direct. Cheap runs encourage slicing, because each slice is one more run. Expensive runs discourage slicing but also discourage the wide, long, expensive run that would give you a decent sample. In both cases the number to watch is not the result. It is the sample size behind it. Put "n =" next to every result in the ledger.

What a sensible test budget looks like
A research budget can be set in plain proportions, whichever pricing model you use. One split that works for many individual traders is to divide your tests into three stages.

Stage 1: Exploration (about half of your runs)
Here you run wide and rough. Try different structures, entry times and exits. Use the longest date range your tool allows. Do not trust any single result. The goal is to find two or three ideas worth a closer look, and to drop the rest. Write down the number of variants you tried.

Stage 2: Stress (about a third)
Take the survivors and try to break them. Add realistic costs and a worse fill than you expect. Shift the entry time by a few minutes. Remove the best ten days and see what is left. Test the other index. Test only the high-VIX sessions. If the idea falls apart under small changes, it was a pattern in the noise.

Stage 3: Confirmation (the rest)
Choose one finalist and decide in advance what result would make you drop it. Then run it once on data it has not seen, or forward test it on paper. One run, one decision. If you did the earlier stages honestly, this stage needs very few runs, and that is why a credit plan is fine for it.

The split shows how pricing should map to stage. Flat plans and free plans are natural for Stage 1. Credit-style rationing is natural for Stages 2 and 3, where each run should have a reason. If your tool has one model only, you can imitate the other by self-imposed rules: a cap on exploration runs per week, or a rule that a confirmation run needs a written hypothesis first.

Costs inside the test matter as much as costs of the tool
One more link between price and research is easy to miss. The cost model inside your backtest decides whether the result is worth anything, and it is often a setting you can leave at zero. Brokerage, exchange charges, STT on option sales, GST and slippage all reduce what you keep. For short-option strategies that trade a small premium and exit at stop, slippage on the stop is usually the largest of these. Check the current charge schedule on your broker's site and on the exchange sites, such as nseindia.com, rather than using a number from memory.

Here is why this belongs in an article about pricing. A tool that makes it easy to leave costs at zero will, on average, produce more optimistic strategies. A tool that forces you to enter costs will produce fewer. The effect is similar to the trial-count effect: a quiet default tilts every result in the same direction. Whichever platform you use, set realistic costs before the first exploration run, not after you have found a winner. If you add costs at the end, you will find that the edge you chose is thinner than you thought, and you will already be attached to it.

As a rough guide to scale, consider an illustrative short straddle where you collect 100 points of premium on a lot of 65. That is Rs 6,500 of gross premium. If total friction (charges plus slippage on both legs, entry and exit) is an average of only 3 points, that is about Rs 195 per trade, or 3% of premium. If a stop-out adds 5 points of slippage on one leg, the friction rises to 8 points, or Rs 520, which is 8% of premium. Over a hundred trades, those are not rounding errors. They are the difference between a flat strategy and a losing one.

Reading backtests, yours and other people's
The same lens helps when you read a result someone else has published, whether on a forum, a vendor page or a social media thread. Ask five questions. How many variants were tried before this one was chosen? How many trades are behind the number? What date range was used, and was it chosen before or after seeing the results? Which costs were included? Which limits of the tool applied?

Most published backtests answer none of these. That does not mean they are wrong. It means you cannot tell. A result that comes with its trial count, sample size and cost settings is rare and worth more than a prettier curve that comes with none.

The same applies to your own work, six months later. When EliteAlgo publishes a study, such as the 163-session desk data used above, the useful parts are the sample size, the date range and the entry rule, and those are the parts to check first. A note that says "looked good in backtest" is useless. A ledger line that says "best of 37 variants, 163 sessions, costs on, 2 points stop slippage" lets you decide again with full information.

Choosing a tool by research behaviour
If you are picking a platform today, here is a short test list that has nothing to do with brand names.

First, find out the marginal cost of one more run in money, time and limits, and be honest about which habit it pushes you toward. Second, check whether costs and slippage are on by default or must be added. Third, check whether you can export the trade list so you can count samples and recompute statistics yourself. Fourth, check whether the tool keeps a history of your runs, because that makes a ledger much easier. Fifth, check how the tool treats dates: can you hold back a block of data and keep yourself from looking at it?

Then run the same simple strategy on two tools and compare the trade lists, not just the totals. Differences in fills, data and costs show up there. A tool that matches your live experience on a one-lot paper run is worth more than a tool with twice the features.

Frequently Asked Questions
Is a free backtesting tool good enough for option selling?

It can be good enough to learn the platform and to screen ideas. For a strategy you plan to trade with real money, check what the free tier limits: history length, legs, runs, exports. A result from a narrowed test should be repeated on a fuller set-up before you rely on it.

How many backtests should I run before going live?

There is no magic count. What matters is that you know the count, record it, and discount the winner for it. A handful of well-planned runs with a clear hypothesis beats hundreds of random sweeps. Always finish with a forward test on paper.

Why does my backtest look great but my live results are worse?

The common causes are best-of-many selection, costs set too low, unrealistic fills on stops, and a short or lucky date range. Start by checking your trial count and your cost settings. Then compare live fills with the fill assumed in the test.

Is credit-based pricing better than a monthly subscription?

Neither is better in general. Credits slow you down and can make you protect early ideas. Subscriptions speed you up and can invite over-testing. Pick the one that counters your own habit, and add the opposite rule yourself.

How do I avoid overfitting a strategy in a backtest?

Keep a trial ledger, test over a long range, change one thing at a time, stress the result with worse costs and shifted entry times, and confirm once on unseen data. A good introduction to the concept is available on investopedia.com.

Do backtest results include brokerage and taxes?

Only if the tool lets you enter them and you do. Many settings default to zero. Always check, and use the current charge schedule from your broker and the exchange.

Conclusion
The price you pay for a backtest decides how often you run one, and how often you run one decides how much of your result is luck. Credits ration you into protecting early ideas. Flat plans free you to over-test. Free plans narrow your design without telling you.

You cannot change the pricing, but you can change your habit. Keep a trial ledger. Put the sample size beside every result. Set realistic costs before the first run. Split your runs into explore, stress and confirm. These steps cost nothing on any platform, and they matter more than the choice of platform.

Algo trading carries a real risk of loss, and a good backtest does not remove it. EliteAlgo is analytical software and not an investment adviser, and nothing in this article is investment advice. The numbers above are either illustrative examples, standard statistical results or figures from a past-data study, and none of them predicts future returns.